General Python snippets

.Python_general_snippets.md

General Python snippets

OBS: These snippets will not run correctly since it was taken from the code in the way it was written. Therefore, the variables do not convey any meaning. Use it just a guide and not a copy-and-paste reference.

log_config.py

import logging

# 1. DEBUG: Detailed information, typically of interest only when diagnosing problems. # Lowest lever
# 2. INFO: Confirmation that things are working as expected.
# 3. WARNING: An indication that something unexpected happened, or indicative of some problem in the near future (e.g. ‘disk space low’). The software is still working as expected.
# 4. ERROR: Due to a more serious problem, the software has not been able to perform some function.
# 5. CRITICAL: A serious error, indicating that the program itself may be unable to continue running. # Highest lever

logging.basicConfig(filename='TC3.log', level=logging.DEBUG, format='%(filename)s:%(message)s', filemode='w', force = True)

nice_confusion_matrix.py

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

for method, method_name in zip((LSC_LPC, LSC_PSD, LSC_PSD_BC), ('LSC+LPC', 'LSC+PSD', 'LSC+PSD+BC')):
    for conf_mat in 'worst confision matrix', 'best confusion matrix':
        df_conf_mat = pd.DataFrame(method[conf_mat], index=all_commands, columns=all_commands)
        fig = plt.figure(figsize = (10,7))
        ax = fig.gca()
        plt.title(f'{conf_mat.capitalize()} - {method_name}')
        ax = sns.heatmap(df_conf_mat, annot=True, cmap="YlGnBu", cbar_kws={"orientation": "horizontal"})
        ax.set_xlabel('Labels')
        ax.set_ylabel('Classifications')

nice_histogram.py

from matplotlib import pyplot as plt

# histogram of the best residue
e_best = all_e[iAIC_best]
fig = plt.figure(figsize=(12, 9), dpi=80)
ax = fig.gca()
ax.set_axisbelow(True)
ax.hist(e_best, bins=10, density=True, alpha=0.75)
plt.xlabel(r'$\xi(n) = y(n)-ŷ(n)$')
plt.ylabel('Estimated probability density function')
plt.title('Histogram of the best residue')
plt.grid(True, linestyle='-.', linewidth=.5)
plt.show()

nice_plot.py

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12, 9), dpi=80)
ax = fig.gca()
ax.set_axisbelow(True)
ax.plot(y_hat[:N], 'y--', zorder=2, label='$\hat{y}[k]$')
ax.plot(y[n_best:N], 'k', zorder=1, label='$y[k]$')
ax.set_xticks(arange(furnas[:3*12].size), labels=('jan', 'fev', 'mar', 'abr', 'mai', 'jun', 'jul', 'ago', 'set', 'out', 'nov', 'dez')*3, rotation=45, fontsize=15)
ax.legend(loc="upper right")
ax.grid(True, linestyle='-.', linewidth=.5)
plt.xlabel(r'$k$')
plt.title('OLS algorithm output signal')
plt.show()

nice_stem.py

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12, 9), dpi=80)
ax = fig.gca()
ax.set_axisbelow(True)
ax.stem(all_data['avancar_4_p10_s1a']['all_a'][:100], markerfmt='o', label='Mine')
ax.stem(all_data['avancar_4_p10_s1a']['all_a_hat'][:100], markerfmt='x', label='Built-in function')
plt.legend(loc="upper right")
plt.title('Yule-Walker comparison of AR(p) coefficients:\nMy from-scratch code vs. built-in Python function')
plt.grid(True, linestyle='-.', linewidth=.5)
plt.show()

nice_subplot.py

import matplotlib.plot as plt

fig, axs = plt.subplots(2, 1, figsize=(8, 8))

axs[0].stem(np.abs(c_k_class1[:200]))
axs[0].set_title('Class 1')

axs[1].stem(np.abs(c_k_class2[:200]))
axs[1].set_title('Class 2')

nice_table.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12, 3), dpi=80)
ax = fig.gca()

# hide axes
fig.patch.set_visible(False)
ax.axis('off')
ax.axis('tight')
col_labels = [f'ARX{n,m}' for n, m in zip(all_n, all_m)]
row_labels = ['AIC', 'BIC']
cell_tex = [[f'{i:.2e}' for i in x] for x in (all_AIC, all_BIC)]

table_ = ax.table(cellText=cell_tex, colLabels=col_labels, loc='center', cellLoc='center', rowLabels=row_labels)
table_.scale(1, 4)
plt.show()

plot_font_format.py

from matplotlib import pyplot as plt

plt.rcParams.update({'font.family': 'DejaVu Sans', 'font.weight': 'normal', 'font.size': 22})